In the context of credit scoring, Credit Reporting Agencies (CRAs) utilize various methods to categorize, or segment, various sub-populations of a population according to credit related behavior and activities. One such method is segmentation. The objective of segmentation is to define a set of sub-populations that when modeled individually and then combined, rank risk more effectively than a single model.
The premise of segmentation is that credit attributes, or characteristics, (independent variables) have a different relationship with risk (dependent variable) for different sub-populations. By identifying the appropriate sub-populations, the attributes, or characteristics, that are most predictive in isolating risk are optimized for that group.
Segmentation using partitions of individual attributes as defined by regression tree analysis has been the traditional methodology used for CRA scores. Ultimately, using the attribute-centric, tree-based approach creates a rank ordering system resulting from a number of nodes (tree endpoints) with differing bad rates. Newer methods incorporate risk-based scores, which are more effective at rank ordering than individual attributes and produce more homogeneous risk sub-populations.
The latest methods incorporate profile scores that categorize individuals into sub-populations that reflect the propensity of an individual to experience a specific type of failure mode, such as bankruptcy or default. Traditional regression tree analysis uses a single dependent variable corresponding to the target dependent variable of the final solution (primary dependent variable). Use of the primary dependent variable, however, may result in the definition of sub-optimal partitions of a profile type score.